Solves the linear equation set a*x=b for the unknown x
for square a matrix.

If the data matrix is known to be a particular type then supplying the
corresponding string to assume_a key chooses the dedicated solver.
The available options are

generic matrix

‘gen’

symmetric

‘sym’

hermitian

‘her’

positive definite

‘pos’

If omitted, 'gen' is the default structure.

The datatype of the arrays define which solver is called regardless
of the values. In other words, even when the complex array entries have
precisely zero imaginary parts, the complex solver will be called based
on the data type of the array.

Parameters:

a : (N, N) array_like

Square input data

b : (N, NRHS) array_like

Input data for the right hand side.

sym_pos : bool, optional

Assume a is symmetric and positive definite. This key is deprecated
and assume_a = ‘pos’ keyword is recommended instead. The functionality
is the same. It will be removed in the future.

lower : bool, optional

If True, only the data contained in the lower triangle of a. Default
is to use upper triangle. (ignored for 'gen')

Whether to check that the input matrices contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.

If the input b matrix is a 1D array with N elements, when supplied
together with an NxN input a, it is assumed as a valid column vector
despite the apparent size mismatch. This is compatible with the
numpy.dot() behavior and the returned result is still 1D array.